Summary of On the Robustness Of Cross-concentrated Sampling For Matrix Completion, by Hanqin Cai and Longxiu Huang and Chandra Kundu and Bowen Su
On the Robustness of Cross-Concentrated Sampling for Matrix Completion
by HanQin Cai, Longxiu Huang, Chandra Kundu, Bowen Su
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to matrix completion, specifically addressing the issue of robustness against sparse outliers using cross-concentrated sampling (CCS). By developing a non-convex iterative algorithm called Robust CUR Completion (RCURC), the authors demonstrate improved efficiency and robustness in synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to complete missing data in matrices. The current best method, CCS, is good but has some weaknesses when dealing with sparse outliers. To fix this problem, the researchers created a new algorithm called Robust CUR Completion (RCURC). They tested RCURC on both fake and real data and showed that it works well. |
